The fact that cognitive diversity matters does not mean that if you assemble a group of diverse but thoroughly uninformed people, their collective wisdom will be smarter than an expert’s. But if you can assemble a diverse group of people who possess varying degrees of knowledge and insight, you’re better off entrusting it with major decisions rather than leaving them in the hands of one or two people, no matter how smart those people are. If this is difficult to believe— in the same way that March’s assertions are hard to believe—it’s because it runs counter to our basic intuitions about intelligence and business. Suggesting that the organization with the smartest people may not be the best organization is heretical, particularly in a business world caught up in a ceaseless “war for talent” and governed by the assumption that a few superstars can make the difference between an excellent and a mediocre company. Heretical or not, it’s the truth: the value of expertise is, in many contexts, overrated.

Now, experts obviously exist. The play of a great chess player is qualitatively different from the play of a merely accomplished one. The great player sees the board differently, he processes information differently, and he recognizes meaningful patterns almost instantly. As Herbert A. Simon and W. G. Chase demonstrated in the 1 970s, if you show a chess expert and an amateur a board with a chess game in progress on it, the expert will be able to re-create from memory the layout of the entire game. The amateur won’t. Yet if you show that same expert a board with chess pieces irregularly and haphazardly placed on it, he will not be able to re-create the layout. This is impressive testimony to how thoroughly chess is imprinted on the minds of successful players. But it also demonstrates how limited the scope of their expertise is. A chess expert knows about chess, and that’s it. We intuitively assume that intelligence is fungible, and that people who are excellent at one intellectual pursuit would be excellent at another. But this is not the case with experts. Instead, the fundamental truth about expertise is that it is, as Chase has said, “spectacularly narrow.”

More important, there’s no real evidence that one can become expert in something as broad as “decision making” or “policy” or “strategy.” Auto repair; piloting, skiing, perhaps even management: these are skills that yield .to application, hard work, and native talent. But forecasting an ncertain future and deciding the best course of action in the face of that future are much less likely to do so. And much of what we’ve seen so far suggests that a large group of diverse individuals will come up with better and more robust forecasts and make more intelligent decisions than even the most skilled “decision maker.”

We’re all familiar with the absurd predictions that business titans have made: Henry Warner of Warner Bros. pronouncing in 1927, “Who the hell wants to hear actors talk?,” or Thomas Watson of IBM declaring in 1943, “I think there is a world market for maybe five computers.” These can be written off as amusing anomalies, since over the course of a century, some smart people are bound to say some dumb things. What can’t be written off, though, is the dismal performance record of most experts.

Between 1984 and 1999, for instance, almost 90 percent of mutual-fund managers underperformed the Wilshire 5000 Index, a relatively low bar. The numbers for bond-fund managers are similar: in the most recent five-year period, more than 95 percent of all managed bond funds underperformed the market. After a survey of expert forecasts and analyses in a wide variety of fields, Wharton professor J. Scott Armstrong wrote, “I could find no studies that showed an important advantage for expertise.” Experts, in some cases, were a little better at forecasting than laypeople (although a number of studies have concluded that nonpsychologists, for instance, are actually better at predicting people’s behavior than psychologists are), hut above a low level, Armstrong concluded, “expertise and accuracy are unrelated.” James Shanteau is one of the country’s leading thinkers on the nature of expertise, and has spent a great deal of time coming up with a method for estimating just how expert someone is. Yet even he suggests that “experts’ decisions are seriously flawed.”

Shanteau recounts a series of studies that have found experts’ judgments to be neither consistent with the judgments of other experts in the field nor internally consistent. For instance, the between-expert agreement in a host of fields, including stock picking, livestock judging, and clinical psychology, is below 50 percent, meaning that experts are as likely to disagree as to agree. More disconcertingly, one study found that the internal consistency of medical pathologists’ judgments was just 0.5, meaning that a pathologist presented with the same evidence would, half the time, offer a different opinion. Experts are also surprisingly bad at what social scientists call “calibrating” their judgments. If your judgments are well calibrated, then you have a sense of how likely it is that your judgment is correct. But experts are much like normal people: they routinely overestimate the likelihood that they’re right.

A survey on the question of overconfidence by economist Terrance Odean found that physicians, nurses, lawyers, engineers, entrepreneurs, and investment bankers all believed that they knew more than they did. Similarly, a recent study of foreign-exchange traders found that 70 percent of the time, the traders overestimated the accuracy of their exchange-rate predictions. In other words, it wasn’t just that they were wrong; they also didn’t have any idea how wrong they were. And that seems to be the rule among experts. The only forecasters whose judgments are routinely well calibrated are expert bridge players and weathermen. It rains on 30 percent of the days when weathermen have predicted a 30 percent chance of rain.

Armstrong, who studies expertise and forecasting, summarized the case this way: ‘One would expect experts to have reliable information for predicting thange and to be able to utilize the information effectively. However, expertise beyond a minimal level is of little value in forecasting change.” Nor was there evidence that even if most experts were not very good at forecasting, a few titans were excellent. Instead, Armstrong wrote, “claims of accuracy by a single expert would seem to be of no practical value.” This was the origin of Armstrong’s “seer-sucker theory”: “No matter how much evidence exists that seers do not exist, suckers will pay for the existence of seers.”

Again, this doesn’t mean that well-informed, sophisticated analysts are of no use in making good decisions. (And it certainly doesn’t mean that you want crowds of amateurs trying to collectively perform surgery or fly planes.) It does mean that however well-informed and sophisticated an expert is, his advice and predictions should be pooled with those of others to get the most out of him. (The larger the group, the more reliable its judgment will be.) And it means that attempting to “chase the expert,” looking for the one man who will have the answers to an organization’s problem, is a waste of time. We know that the group’s decision will consistently be better than most of the people in the group, and that it will be better decision after decision, while the performance of human experts will vary dramatically depending on the problem they’re asked to solve. So it is unlikely that one person, over time, will do better than the group.

Now, it’s possible that a small number of genuine ecperts— that is, people who can consistently offer better judgments than those of a diverse, informed group—do exist. The investor Warren Buffett, who has consistently outperformed the S&P 500 Index since the 1960s, is certainly someone who comes to mind. The problem is that even if these superior beings do exist, there is no easy way to identify them. Past performance, as we are often told, is no guarantee of future results. And there are so many would-be experts out there that distinguishing between those who are lucky and those who are genuinely good is often a near-impossible task. At the very least, it’s a job that requires considerable patience: if you wanted to be sure that a successful money manager was beating the market because of his superior skill, and not because of luck or measurement error, you’d need many years, if not decades, of data. And if a group is so unintelligent that it will flounder without the right expert, it’s not clear why the group would be intelligent enough to recognize an expert when it found him.

We think that experts will, in some sense, identify themselves, announcing their presence ad demonstrating their expertise by their level of confidence. But it doesn’t work that way. Strangely, experts are no more confident in their abilities than average people are, which is to say that they are overconfident like everyone else, but no more so. Similarly, there is very little correlation between experts’ self-assessment and their performance. Knowing and knowing that you know are apparently two very different skills.

If this is the case, then why do we cling so tightly to the idea that the right expert will save us? And why do we ignore the fact that simply averaging a group’s estimates will produce a very good result? Richard Larrick and Jack B. Soil suggest that the answer is that we have bad intuitions about averaging. We assume averaging means dumbing down or compromising. When people are faced with the choice of picking one expert or picking pieces of advice from a number of experts, they try to pick the best expert rather than simply average across the group. Another reason, surely, is our assumption that true intelligence resides only in individuals, so that finding the right person—the right consultant, the right CEO—will make all the difference. In a sense, the crowd is blind to its own wisdom. Finally, we seek out experts because we get, as the writer Nassim Taleb asserts, “fooled by randomness.” If there are enough people out there making predictions, a few of them are going to compile an impressive record over time. That does not mean that the record was the product of skill, nor does it mean that the record will continue into the future. Again, trying to find smart people will not lead you astray. Trying to find the smartest person will.